Multiobjective Optimization Model for Transit Fleet Resource Allocation

Abstract

State and local transit agencies require government support to preserve their aging transit fleets. With the passage of time, transit fleets get older and require maintenance costs to keep them operational. To provide services at a desired level, transit agencies must maintain a minimum fleet size. Two imperative considerations from the transit planning viewpoint are (a) the remaining life of the total fleet and (b) the cost required to maintain the fleet size. While the former is a quality measure indicating the health of the fleet, the latter is an economic measure requiring minimum expenditure levels. Ideally, agencies would like to maximize the total remaining life of the fleet and minimize the total cost required to maintain the fleet size. In this paper, a multiobjective optimization (MO) model is proposed to incorporate simultaneously the two objectives when subjected to budget and various operational constraints. The MO problem is solved with a classical weight sum approach by using the branch and bound algorithm, which has proved to be better than other solution methodologies. The MO results in Pareto-optimal solutions with possible trade-offs between the two objectives. The model is applied to a large-scale transit fleet system in the state of Michigan. The case study results demonstrate that the proposed model is compact, efficient, robust, and suitable for long-range planning with multiple solutions to choose from a Pareto-optimal frontier. The correlation between decision variables and objective functions has been investigated in-depth, providing important insights. The proposed model can act as a tool for resource allocation for state and local agency transit fleets.

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